Edge Detection for Satellite Images without Deep Networks

• URL: http://arxiv.org/abs/2105.12633v1
• Date: Wed, 26 May 2021 15:47:42 GMT
• Title: Edge Detection for Satellite Images without Deep Networks
• Authors: Joshua Abraham, Calden Wloka
• Abstract summary: Recent approaches to satellite image analysis have largely emphasized deep learning methods. Deep learning has some drawbacks, including the requirement of specialized computing hardware. The cost of both computational resources and training data annotation may be prohibitive when dealing with large satellite datasets.
• Score: 2.741266294612776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Satellite imagery is widely used in many application sectors, including agriculture, navigation, and urban planning. Frequently, satellite imagery involves both large numbers of images as well as high pixel counts, making satellite datasets computationally expensive to analyze. Recent approaches to satellite image analysis have largely emphasized deep learning methods. Though extremely powerful, deep learning has some drawbacks, including the requirement of specialized computing hardware and a high reliance on training data. When dealing with large satellite datasets, the cost of both computational resources and training data annotation may be prohibitive.

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